The cosine measure of a set of vectors in \(\mathbb{R}^n\) measures how well the set covers all directions in \(\mathbb{R}^n\). It identifies the direction furthest, in angle, from the set. It is used in the convergence theory of various optimization algorithms, but also highlights interesting geometric properties of sets. For example, the cosine measure of a set \(S\) is greater than zero if and only if given any \(\mathcal{C}^1\) function \(f\) at a point \(x\) where the gradient is nonzero, \(S\) must contain a descent direction of \(f\) at \(\mathbf{x}\). In this paper, we examine the question of what can be said when the function \(f\) is non-differentiable or if it has a gradient equal to the zero vector. To examine these cases, we introduce the novel concept of the {\em cosine measure of a function} at a point. This value provides an infimum on the value of the cosine measure that a set of vectors requires to guarantee it contains a descent direction of the function at the point of interest. We present mathematical theory around this concept, including examples showing that the cosine measure of a smooth function can have any value in \([-1,1]\). We further present algorithms to compute the cosine measure of a function, and examples demonstrating the algorithm on smooth and nonsmooth functions. These results also shed light on the the cosine measure of infinite sets and nonconvex cones.
The cosine measure of a function at a point
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